Since April 2018, the TROPOspheric Monitoring Instrument (TROPOMI) has provided data on tropospheric NO₂ column concentrations (C_TROPOMI) with unprecedented spatial resolution. This study aims to assess the capability of TROPOMI to acquire high spatial resolution data regarding surface NO₂ mixing ratios. In general, the instrument effectively detected major and moderate sources of NO₂ over South Korea with a clear weekday–weekend distinction. We compared the C_TROPOMI with surface NO₂ mixing ratio measurements from an extensive ground-based network over South Korea operated by the Korean Ministry of Environment (S_KME; more than 570 sites), for 2019. Spatiotemporally collocated C_TROPOMI and S_KME showed a moderate correlation (correlation coefficient, r = 0.67), whereas their annual mean values at each site showed a higher correlation (r = 0.84). The C_TROPOMI and S_KME were well correlated around the Seoul metropolitan area, where significant amounts of NO₂ prevailed throughout the year, whereas they showed lower correlation at rural sites. We converted the tropospheric NO₂ from TROPOMI to the surface mixing ratio (S_TROPOMI) using the EAC4 (ECMWF Atmospheric Composition Reanalysis 4) profile shape, for quantitative comparison with the S_KME. The estimated S_TROPOMI generally underestimated the in-situ value obtained, S_KME (slope = 0.64), as reported in previous studies.

The Korea–United States Air Quality (KORUS-AQ) campaign is a joint study between the United States National Aeronautics and Space Administration (NASA) and the South Korea National Institute of Environmental Research (NIER) to monitor megacity and transboundary air pollution around the Korean Peninsula using airborne and ground-based measurements. Here, tropospheric nitrogen dioxide (NO₂) slant column density (SCD) measurements were retrieved from Geostationary Trace and Aerosol Sensor Optimization (GeoTASO) L1B data during the KORUS-AQ campaign (2 May to 10 June 2016). The retrieved SCDs were converted to tropospheric vertical column densities using the air mass factor (AMF) obtained from a radiative transfer calculation with trace gas profiles and aerosol property inputs simulated with the Community Multiscale Air Quality (CMAQ) model and surface reflectance data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). For the first time, we examine highly resolved (250 m × 250 m resolution) tropospheric NO₂ over the Seoul and Busan metropolitan regions and the industrial region of Anmyeon. We reveal that the maximum NO₂ vertical column densities (VCDs) were 4.94×10¹⁶ and 1.46×10¹⁷ molec. cm⁻² at 09:00 and 15:00 LT over Seoul, respectively, 6.86×10¹⁶ and 4.89×10¹⁶ molec. cm⁻² in the morning and afternoon over Busan, respectively, and 1.64×10¹⁶ molec. cm⁻² over Anmyeon. The VCDs retrieved from the GeoTASO airborne instrument were correlated with those obtained from the Ozone Monitoring Instrument (OMI) (r=0.48), NASA's Pandora Spectrometer System (r=0.91), and NO₂ mixing ratios obtained from in situ measurements (r=0.07 in the morning, r=0.26 in the afternoon over the Seoul, and r>0.56 over Busan). Based on our results, GeoTASO is useful for identifying NO₂ hotspots and their spatial distribution in highly populated cities and industrial areas.

Atmospheric carbon monoxide (CO) significantly impacts climate change and human health, and has become the focus of increased air quality and climate research. Since 2018, the Troposphere Monitoring Instrument (TROPOMI) has provided total column amounts of CO (C_TROPOMI) with a high spatial resolution to monitor atmospheric CO. This study compared and assessed the accuracy of C_TROPOMI measurements using surface in-situ measurements (S_KME) obtained from an extensive ground-based network over South Korea, where CO level is persistently affected by both local emissions and trans-boundary transport. Our analysis reveals that the TROPOMI effectively detected major emission sources of CO over South Korea and efficiently complemented the spatial coverage of the ground-based network. In general, the correlations between C_TROPOMI and S_KME were lower than those for NO₂ reported in a previous study, and this discrepancy was partly attributed to the lower spatiotemporal variability. Moreover, vertical CO profiles were sampled from the ECMWF CAMS reanalysis data (EAC4) to convert C_TROPOMI to surface mixing ratios (S_TROPOMI). S_TROPOMI showed a significant underestimation compared with S_KME by approximately 40%, with a moderate correlation of approximately 0.51. The low biases of S_TROPOMI were more significant during the winter season, which was mainly attributed to the underestimation of the EAC4 CO at the surface. This study can contribute to the assessment of satellite and model data for monitoring surface air quality and greenhouse gas emissions.

In order to investigate the abundance of and temporal variation in nitrogen dioxide (NO₂) in the troposphere and validate the corresponding satellite products during a normal year and the lockdown period of coronavirus disease 2019 (COVID-19) in Lhasa, a city on the Tibetan Plateau (TP), ground-based remote-sensing measurements captured by applying multi-axis differential optical absorption spectroscopy (MAX-DOAS) were recorded from August 2021 to March 2023 at the Lhasa site (91.14°E, 29.66°N; 3552.5 m altitude). The NO₂ differential slant column densities (dSCDs) were retrieved from the spectra of scattered solar light at different elevation angles. Then, the tropospheric NO₂ vertical column densities (VCDs) were calculated with the geometric approximation method. Based on the retrieved tropospheric NO₂ VCDs, we found that the pattern of monthly variation in tropospheric NO₂ VCDs in Lhasa presented two peaks, one in winter and one around May. According to the monthly means of tropospheric NO₂ VCDs during the COVID-19 lockdown, the NO₂ background level in Lhasa was estimated to be 0.53 × 10¹⁵ molecules·cm⁻². For diurnal variations in tropospheric NO₂ VCDs, the morning and evening peaks disappeared during the COVID-19 lockdown period. The east–west direction (i.e., along the river valley) was the main path of NO₂ transport and dispersion in Lhasa, but the tropospheric NO₂ VCDs were little dependent on the wind direction or wind speed during the COVID-19 lockdown. The correlation coefficient of tropospheric NO₂ VCDs was R = 0.33 (R = 0.43), with the averaged relative deviation of −28% (99%) for the TROPOMI (GEMS) relative to ground-based MAX-DOAS. The monthly deviations of tropospheric NO₂ VCDs between ground-based MAX-DOAS and the satellite showed a dependence on NO₂ abundance, with the maxima of the monthly positive deviations during the COVID-19 lockdown period. The GEMS could not capture the strong and systematic diurnal variation in tropospheric NO₂ VCDs in the “normal” year well. During the COVID-19 lockdown, the GEMS (>2 × 10¹⁵ molecules·cm⁻²) overestimated the hourly levels measured by ground-based MAX-DOAS (<1.6 × 10¹⁵ molecules·cm⁻²). As a whole, these results are beneficial to understanding the influences of anthropogenic activities on NO₂ background levels in Lhasa and to learning the accuracy of satellite products over the TP, with its high altitude and complex terrain.

Nitrogen oxides (NO_x) are key precursors of tropospheric ozone and particulate matter. The sparse local observations make it challenging to understand NO_x cycling across the Tibetan Plateau (TP), which plays a crucial role in regional and global atmospheric processes. Here, we utilized Geostationary Environment Monitoring Spectrometer (GEMS) data to examine the tropospheric NO₂ vertical column density (Ω_NO2) spatiotemporal variability over TP, a pristine environment marked with natural sources. GEMS observations revealed that the Ω_NO2 over TP is generally low compared with surrounding regions with significant surface emissions, such as India and the Sichuan basin. A spatial decreasing trend of Ω_NO2 is observed from the south and center to the north over Tibet. Unlike the surrounding regions, the TP exhibits opposing seasonal patterns and a negative correlation between the surface NO₂ and Ω_NO2. In the Lhasa and Nam Co areas within Xizang, the highest Ω_NO2 in spring contrasts with the lowest surface concentration. Diurnally, a midday increase in Ω_NO2 in the warm season reflects some external sources affecting the remote area. Trajectory analysis suggests strong convection lifted air mass from India and Southeast Asia into the upper troposphere over the TP. These findings highlight the mixing interplay of nonlocal and local NO_x sources in shaping NO₂ variability in a high-altitude environment. Future research should explore these transport mechanisms and their implications for atmospheric chemistry and climate dynamics over the TP.

Nitrogen dioxide (NO2) and nitrogen oxide (NO), usually referred to as nitrogen oxides (NOx), are emitted into the atmosphere by anthropogenic and natural sources. The detection and monitoring of NO2 plays a key role in air quality managements because of its effects on health and of its contribution to the increase of tropospheric ozone and nitrate aerosols. Unfortunately, up to now, observations were possible only once a day based on satellites in low-earth orbit (GOME, GOME-2, OMI and TROPOMI). However, from now on, it is possible to observe the diurnal variations over the Asia based on Geostationary Environment Monitoring Spectrometer (GEMS) in geostationary earth orbit. Here, we present results of tropospheric nitrogen dioxide column observations with high temporal (hourly) and spatial resolutions over major cities in Asia. In addition, we evaluate the GEMS NO2 operational algorithm by comparing GEMS total and tropospheric NO2 columns with independent observations from ground-based Pandora (total column) and MAX-DOAS (tropospheric column). Additionally, we retrieved the GEMS tropospheric NO2 columns by subtracting the stratospheric NO2 columns, which are assumed based on SLIMCAT model data and then scalded with the real GEMS observations, from the total NO2 columns. Then we also compared the tropospheric NO2 columns that are retrieved based on GEMS NO2 operational algorithm and SLIMCAT model, respectively. Lastly, we compared the GEMS NO2 with other low-earth orbit satellite instruments that include OMI and TROPOMI.